Benchmarking Deep Reinforcement Learning for Continuous Control

Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1329-1338, 2016.

Abstract

Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at https://github.com/rllab/rllab in order to facilitate experimental reproducibility and to encourage adoption by other researchers.

Cite this Paper


BibTeX
@InProceedings{pmlr-v48-duan16, title = {Benchmarking Deep Reinforcement Learning for Continuous Control}, author = {Duan, Yan and Chen, Xi and Houthooft, Rein and Schulman, John and Abbeel, Pieter}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {1329--1338}, year = {2016}, editor = {Balcan, Maria Florina and Weinberger, Kilian Q.}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/duan16.pdf}, url = { http://proceedings.mlr.press/v48/duan16.html }, abstract = {Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at https://github.com/rllab/rllab in order to facilitate experimental reproducibility and to encourage adoption by other researchers.} }
Endnote
%0 Conference Paper %T Benchmarking Deep Reinforcement Learning for Continuous Control %A Yan Duan %A Xi Chen %A Rein Houthooft %A John Schulman %A Pieter Abbeel %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-duan16 %I PMLR %P 1329--1338 %U http://proceedings.mlr.press/v48/duan16.html %V 48 %X Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at https://github.com/rllab/rllab in order to facilitate experimental reproducibility and to encourage adoption by other researchers.
RIS
TY - CPAPER TI - Benchmarking Deep Reinforcement Learning for Continuous Control AU - Yan Duan AU - Xi Chen AU - Rein Houthooft AU - John Schulman AU - Pieter Abbeel BT - Proceedings of The 33rd International Conference on Machine Learning DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-duan16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 48 SP - 1329 EP - 1338 L1 - http://proceedings.mlr.press/v48/duan16.pdf UR - http://proceedings.mlr.press/v48/duan16.html AB - Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at https://github.com/rllab/rllab in order to facilitate experimental reproducibility and to encourage adoption by other researchers. ER -
APA
Duan, Y., Chen, X., Houthooft, R., Schulman, J. & Abbeel, P.. (2016). Benchmarking Deep Reinforcement Learning for Continuous Control. Proceedings of The 33rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 48:1329-1338 Available from http://proceedings.mlr.press/v48/duan16.html .

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